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/libafl

Builds custom fuzzers with LibAFL, the modular Rust fuzzing library. Covers composing observers, feedbacks, mutators, schedulers, and executors into a fuzzer for targets the standard tools do not fit. Use when writing a bespoke fuzzer or mutator, fuzzing a non-standard target or

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SKILL.md

libafl.SKILL.md
name: libafl
type: fuzzer
description: "Builds custom fuzzers with LibAFL, the modular Rust fuzzing library. Covers composing observers, feedbacks, mutators, schedulers, and executors into a fuzzer for targets the standard tools do not fit. Use when writing a bespoke fuzzer or mutator, fuzzing a non-standard target or architecture, implementing a fuzzing research idea, or when libFuzzer and AFL++ lack the control you need."

LibAFL

LibAFL is a modular fuzzing library that implements features from AFL-based fuzzers like AFL++. Unlike traditional fuzzers, LibAFL provides all functionality in a modular and customizable way as a Rust library. It can be used as a drop-in replacement for libFuzzer or as a library to build custom fuzzers from scratch.

When to Use

| Fuzzer | Best For | Complexity | |--------|----------|------------| | libFuzzer | Quick setup, single-threaded | Low | | AFL++ | Multi-core, general purpose | Medium | | LibAFL | Custom fuzzers, advanced features, research | High |

**Choose LibAFL when:**

  • You need custom mutation strategies or feedback mechanisms
  • Standard fuzzers don't support your target architecture
  • You want to implement novel fuzzing techniques
  • You need fine-grained control over fuzzing components
  • You're conducting fuzzing research

Quick Start

LibAFL can be used as a drop-in replacement for libFuzzer with minimal setup:

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
    // Call your code with fuzzer-provided data
    my_function(data, size);
    return 0;
}

Build LibAFL's libFuzzer compatibility layer:

git clone https://github.com/AFLplusplus/LibAFL
cd LibAFL/libafl_libfuzzer_runtime
./build.sh

Compile and run:

clang++ -DNO_MAIN -g -O2 -fsanitize=fuzzer-no-link libFuzzer.a harness.cc main.cc -o fuzz
./fuzz corpus/

Installation

Prerequisites

  • Clang/LLVM 15-18
  • Rust (via rustup)
  • Additional system dependencies

Linux/macOS

Install Clang:

apt install clang

Or install a specific version via apt.llvm.org:

wget https://apt.llvm.org/llvm.sh
chmod +x llvm.sh
sudo ./llvm.sh 15

Configure environment for Rust:

export RUSTFLAGS="-C linker=/usr/bin/clang-15"
export CC="clang-15"
export CXX="clang++-15"

Install Rust:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Install additional dependencies:

apt install libssl-dev pkg-config

For libFuzzer compatibility mode, install nightly Rust:

rustup toolchain install nightly --component llvm-tools

Verification

Build LibAFL to verify installation:

cd LibAFL/libafl_libfuzzer_runtime
./build.sh
# Should produce libFuzzer.a

Writing a Harness

LibAFL harnesses follow the same pattern as libFuzzer when using drop-in replacement mode:

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
    // Your fuzzing target code here
    return 0;
}

When building custom fuzzers with LibAFL as a Rust library, harness logic is integrated directly into the fuzzer. See the "Writing a Custom Fuzzer" section below for the full pattern.

> **See Also:** For detailed harness writing techniques, see the **harness-writing** technique skill.

Usage Modes

LibAFL supports two primary usage modes:

1. libFuzzer Drop-in Replacement

Use LibAFL as a replacement for libFuzzer with existing harnesses.

**Compilation:**

clang++ -DNO_MAIN -g -O2 -fsanitize=fuzzer-no-link libFuzzer.a harness.cc main.cc -o fuzz

**Running:**

./fuzz corpus/

**Recommended for long campaigns:**

./fuzz -fork=1 -ignore_crashes=1 corpus/

2. Custom Fuzzer as Rust Library

Build a fully customized fuzzer using LibAFL components.

**Create project:**

cargo init --lib my_fuzzer
cd my_fuzzer
cargo add libafl@0.13 libafl_targets@0.13 libafl_bolts@0.13 libafl_cc@0.13 \
  --features "libafl_targets@0.13/libfuzzer,libafl_targets@0.13/sancov_pcguard_hitcounts"

**Configure Cargo.toml:**

[lib]
crate-type = ["staticlib"]

Writing a Custom Fuzzer

> **See Also:** For detailed harness writing techniques, patterns for handling complex inputs, > and advanced strategies, see the **fuzz-harness-writing** technique skill.

Fuzzer Components

A LibAFL fuzzer consists of modular components:

1. **Observers** - Collect execution feedback (coverage, timing) 2. **Feedback** - Determine if inputs are interesting 3. **Objective** - Define fuzzing goals (crashes, timeouts) 4. **State** - Maintain corpus and metadata 5. **Mutators** - Generate new inputs 6. **Scheduler** - Select which inputs to mutate 7. **Executor** - Run the target with inputs

Basic Fuzzer Structure

use libafl::prelude::*;
use libafl_bolts::prelude::*;
use libafl_targets::{libfuzzer_test_one_input, std_edges_map_observer};

#[no_mangle]
pub extern "C" fn libafl_main() {
    let mut run_client = |state: Option<_>, mut restarting_mgr, _core_id| {
        // 1. Setup observers
        let edges_observer = HitcountsMapObserver::new(
            unsafe { std_edges_map_observer("edges") }
        ).track_indices();
        let time_observer = TimeObserver::new("time");

        // 2. Define feedback
        let mut feedback = feedback_or!(
            MaxMapFeedback::new(&edges_observer),
            TimeFeedback::new(&time_observer)
        );

        // 3. Define objective
        let mut objective = feedback_or_fast!(
            CrashFeedback::new(),
            TimeoutFeedback::new()
        );

        // 4. Create or restore state
        let mut state = state.unwrap_or_else(|| {
            StdState::new(
                StdRand::new(),
                InMemoryCorpus::new(),
                OnDiskCorpus::new(&output_dir).unwrap(),
                &mut feedback,
                &mut objective,
            ).unwrap()
        });

        // 5. Setup mutator
        let mutator = StdScheduledMutator::new
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libafl installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.